IBM and NNSA's Lawrence Livermore National Laboratory will jointly design a new supercomputer in the Blue Gene family. Called Blue Gene/L, the machine will be at least 15 times faster, 15 times more power efficient and consume about 50 times less space per computation than today's fastest supercomputers.

Blue Gene/L marks a major expansion of the Blue Gene project. Blue Gene/L is expected to operate at about 200 teraflops (200 trillion operations per second) which is larger than the total computing power of the top 500 supercomputers in the world today. Blue Gene/L will also be a part of IBM's research in "autonomic computing'', an initiative to design computer systems that are self-healing, self-managing and self-configuring.

The development of Blue Gene/L will take place as part of work underway to build a petaflop-scale (one quadrillion operations per second) machine, as announced in December 1999.

IBM and Lawrence Livermore have a long history of working together on supercomputing projects, most significantly on the NNSA's Accelerated Strategic Computing Initiative (ASCI) Program. IBM built the "ASCI White'' machine for Livermore, which is the world's current record-breaking supercomputer.

"Our initial exploration made us realize we can expand our Blue Gene project to deliver more commercially viable architectures for a broad customer set, and still accomplish our original goal of protein science simulations,'' said Mark Dean, vice president of systems, IBM Research. "Partnering with Lawrence Livermore is a key part of our strategy, as they bring important application and design expertise to the project.''

Researchers at the national laboratories plan to use Blue Gene/L, which is expected to be completed by 2005, to simulate physical phenomena of national interest -- such as aging of materials, fires, and explosions -- that require computational capability much greater than presently available.

"This represents a new thrust, very different from the approach taken by the main line of ASCI machines. Up until now, ASCI supercomputers have been designed to address the entire spectrum of numerical simulations required of the stockpile stewardship effort,'' said David Nowak, ASCI Program Leader at LLNL. "This new Blue Gene/L innovation can address an important subset of those computational problems, those that can be easily divided to run on many tens of thousands of processors.''

"Examples of those applications include the modeling of the aging and properties of materials, and the modeling of turbulence,'' added Nowak. "This technology opens the door to a number of applications of great interest to civilian industry and business, like biology and other life sciences. The future of US high-performance computing will benefit tremendously from pursuing both of these paths in parallel.''

The architecture for Blue Gene/L is expected to be more easily adaptable to commercial applications than the original Blue Gene project, and promises to be more affordable to business users than the leading-edge supercomputers found at national laboratories. This new approach to supercomputing promises to make the dramatic reductions in power consumption, cost and space requirements needed to turn massively parallel computing into a practical tool for business and industry. As part of the expansion of the Blue Gene project, IBM is actively pursuing a partner to design a companion machine to Blue Gene/L targeted to data-intensive applications commonly found in commercial computing.

New Architecture for Supercomputers -- Not Just For Scientists AnymoreWhile today's machines are amazingly fast number crunchers, many data-intensive applications are slowed because of the time it takes to simply access information from the memory chips. The Blue Gene/L design will run these applications much faster because the machine will be populated with data-chip cells optimized for data access. Each chip includes two processors: one for computing and one for communicating, and its own on-board memory. Each of the data-chip cells will work on a small part of a larger problem. This increase in data access speed will make a huge difference in the kinds of results these machines can produce and the kinds of problems they can solve.

"Machines like Blue Gene/L are designed to handle data-intensive applications like content distribution, simulations, and modeling, webserving, data mining or business intelligence,'' added Dean.

NNSA's Bill Reed, ASCI's national program leader, lists an impressive array of projects that can make use of this new approach and cites "the continuing need for cost-effective computing to address important national security issues. We need to run these problems in days not months and we need to simultaneously support many scientists across all three NNSA laboratories working on a broad spectrum of technical issues. The value to both national security programs and commercial interests can be dramatic, especially in the biological sciences and medical and pharmaceutical fields.''

IBM and Lawrence Livermore will team up to explore the hardware and software components needed to construct this new computing architecture, and Livermore will provide additional design expertise for the applications that can take advantage of the Blue Gene/L machine.

Lawrence Livermore will get help on the Blue Gene/L project from collaborators at the DOE's NNSA, Columbia University, San Diego Supercomputing Center, and Caltech.

ASCI PartnershipsThe ASCI Program at Lawrence Livermore, Los Alamos, and Sandia National Laboratories has been partnering with the supercomputing industry for the past five years in developing a series of supercomputers for NNSA's Stockpile Stewardship Program. This latest effort continues to build on that experience to help enable the US to maintain its nuclear stockpile without underground nuclear testing and make unprecedented contributions to many fields of science that rely heavily on computing and simulation. ASCI's goal is not just to increase the speed of high-performance computing but also to allow scientists to accept challenges that they could not have attempted before the advent of teraflops-scale computers and beyond.